Analysis of Gait Patterns in Neurodegenerative Disorders Among OlderAdults: A Ground Reaction Force Data Approach
Bibliographic record
Abstract
Increasing awareness of walking-related issues leading to falls, particularly in older adults, has highlighted this important concern.Even though walking is a fundamental human movement, studying it is difficult because it involves intricate brain, nerve, and muscle coordination.Neurodegenerative disorders like Amyotrophic Lateral Sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD) are frequently associated with walking limitations, highlighting the critical need for precise diagnostic tools.This study employed a comprehensive approach, delving into the intricate examination of gait patterns in individuals with neurodegenerative disorders.We used ground reaction force (GRF) step data from the Physionet public database, which converted into the time-frequency domain using continuous wavelet transform (CWT).We applied feature extraction techniques to identify unique gait characteristics for each disorder.Our findings revealed significant differences in gait among neurodegenerative diseases, with Parkinson's disease exhibiting the highest variability, ALS showing less variability, and Huntington's disease falling in between.These results illustrate the complex nature of walking issues in neurodegenerative diseases, highlighting the necessity of specific diagnostic approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".